CSV Processing in Python
Read, write, filter, and transform CSV files in Python using csv, pandas, and DictReader.
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Tags: CSV processing Python, Python csv module, pandas read_csv
CSV Processing in Python Python's standard library module and the pandas library together cover every CSV use case — from simple scripts to large-scale data pipelines. The CSV format is defined in RFC 4180 by the IETF, and Python's module follows this specification. --- The csv Module: Reading Always use when opening files for the csv module. This prevents universal newlines mode from interfering with the csv parser's newline handling. The csv Module: Writing What is Filtering Rows with the csv Module? pandas: Reading CSV pandas: Filtering and Transforming pandas: Writing CSV What is Handling Large Files with Chunking? For files too large to fit in memory: What is csv Module vs pandas Comparison? | Feature | csv module | pandas | |---------|-----------|--------| | Dependencies | None…
Frequently Asked Questions
How do I read a CSV file in Python?
Use `csv.reader(open('data.csv'))` for list-per-row access or `csv.DictReader(open('data.csv'))` for dict-per-row access. For analysis, `pandas.read_csv('data.csv')` is faster and adds column operations. Always open with `newline=''` when using the csv module.
How do I write to a CSV in Python?
Use `csv.writer(f)` and call `writer.writerow(row)` for each row. For dicts, use `csv.DictWriter(f, fieldnames=columns)` and call `writer.writeheader()` then `writer.writerow(row)`. Always open with `newline=''` on Windows to avoid double newlines.
What is the csv.DictReader?
csv.DictReader reads each CSV row as an OrderedDict with header names as keys. It infers column names from the first row by default, or from a `fieldnames` parameter. Missing fields return the `restval` value (default None); extra fields go to `restkey` (default None).
How do I filter rows in a CSV with Python?
With the csv module, use a conditional inside a list comprehension: `[row for row in reader if row['status'] == 'active']`. With pandas, use boolean indexing: `df[df['status'] == 'active']`. Pandas is faster for large files.
When should I use pandas vs csv module?
Use the csv module for small files, scripts with no dependencies, or simple row-by-row transforms. Use pandas for analysis, aggregation, multi-column operations, large files (>10k rows), or when you need to join multiple datasets.
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